
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

							if is_vision_available():
														from transformers import MaskFormerImageProcessor

if is_vision_available():
							from PIL import Image

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							Tuple    ,						snake_case_			:							str=2    ,						snake_case_			:							str=True    ,						snake_case_			:							List[Any]=False    ,						snake_case_			:							str=10    ,						snake_case_			:							int=3    ,						snake_case_			:							Optional[int]=32 * 4    ,						snake_case_			:							Union[str, Any]=32 * 6    ,						snake_case_			:							Any=4    ,						snake_case_			:							Dict=32    ,						):
							UpperCamelCase_:				Optional[int]						=    parent
							UpperCamelCase_:				Any						=    batch_size
							UpperCamelCase_:				int						=    is_training
							UpperCamelCase_:				Dict						=    use_auxiliary_loss
							UpperCamelCase_:				List[str]						=    num_queries
							UpperCamelCase_:				Tuple						=    num_channels
							UpperCamelCase_:				Dict						=    min_size
							UpperCamelCase_:				Dict						=    max_size
							UpperCamelCase_:				Tuple						=    num_labels
							UpperCamelCase_:				Optional[int]						=    mask_feature_size

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				List[Any]						=    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
							    snake_case_   )

							UpperCamelCase_:				Tuple						=    torch.ones([self.batch_size, self.min_size, self.max_size]    ,						device=snake_case_   )

							UpperCamelCase_:				List[Any]						=    (
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,						device=snake_case_   ) > 0.5
							).float()
							UpperCamelCase_:				Union[str, Any]						=    (torch.rand((self.batch_size, self.num_labels)    ,						device=snake_case_   ) > 0.5).long()

							UpperCamelCase_:				Tuple						=    self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels

			def    lowerCAmelCase__						(							self			:							str   ):
							return MaskFormerConfig.from_backbone_and_decoder_configs(
							    backbone_config=SwinConfig(
							        depths=[1, 1, 1, 1]    ,						)    ,						decoder_config=DetrConfig(
							        decoder_ffn_dim=128    ,						num_queries=self.num_queries    ,						decoder_attention_heads=2    ,						d_model=self.mask_feature_size    ,						)    ,						mask_feature_size=self.mask_feature_size    ,						fpn_feature_size=self.mask_feature_size    ,						num_channels=self.num_channels    ,						num_labels=self.num_labels    ,						)

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    self.prepare_config_and_inputs()
							UpperCamelCase_:				Tuple						=    {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
							return config, inputs_dict

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							List[Any]   ):
							UpperCamelCase_:				List[str]						=    output.encoder_hidden_states
							UpperCamelCase_:				Optional[int]						=    output.pixel_decoder_hidden_states
							UpperCamelCase_:				List[str]						=    output.transformer_decoder_hidden_states

							self.parent.assertTrue(len(snake_case_   )    ,						len(config.backbone_config.depths   )   )
							self.parent.assertTrue(len(snake_case_   )    ,						len(config.backbone_config.depths   )   )
							self.parent.assertTrue(len(snake_case_   )    ,						config.decoder_config.decoder_layers   )

			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							List[str]    ,						snake_case_			:							int    ,						snake_case_			:							str=False   ):
							with torch.no_grad():
											UpperCamelCase_:				Optional[int]						=    MaskFormerModel(config=snake_case_   )
											model.to(snake_case_   )
											model.eval()

											UpperCamelCase_:				str						=    model(pixel_values=snake_case_    ,						pixel_mask=snake_case_   )
											UpperCamelCase_:				Union[str, Any]						=    model(snake_case_    ,						output_hidden_states=snake_case_   )
							# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
							# encoder and pixel decoder
							self.parent.assertEqual(
							    output.transformer_decoder_last_hidden_state.shape    ,						(self.batch_size, self.num_queries, self.mask_feature_size)    ,						)
							# let's ensure the other two hidden state exists
							self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
							self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

							if output_hidden_states:
											self.check_output_hidden_state(snake_case_    ,						snake_case_   )




			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							List[str]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							List[str]   ):
							UpperCamelCase_:				str						=    MaskFormerForInstanceSegmentation(config=snake_case_   )
							model.to(snake_case_   )
							model.eval()

							def comm_check_on_output(snake_case_			:							List[Any]   ):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
											self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape    ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,						)
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape    ,						(self.batch_size, self.num_queries, self.num_labels + 1)   )

							with torch.no_grad():
											UpperCamelCase_:				int						=    model(pixel_values=snake_case_    ,						pixel_mask=snake_case_   )
											UpperCamelCase_:				int						=    model(snake_case_   )

											comm_check_on_output(snake_case_   )

											UpperCamelCase_:				Dict						=    model(
											    pixel_values=snake_case_    ,						pixel_mask=snake_case_    ,						mask_labels=snake_case_    ,						class_labels=snake_case_   )

							comm_check_on_output(snake_case_   )

							self.parent.assertTrue(result.loss is not None   )
							self.parent.assertEqual(result.loss.shape    ,						torch.Size([1]   )   )




@require_torch
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
			__UpperCamelCase   :    List[str]          =			(
			    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
			    if is_torch_available()
			    else {}
			)

			__UpperCamelCase   :    Union[str, Any]          =			False
			__UpperCamelCase   :    Any          =			False
			__UpperCamelCase   :    List[Any]          =			False
			__UpperCamelCase   :    int          =			False

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				List[str]						=    MaskFormerModelTester(self   )
							UpperCamelCase_:				List[Any]						=    ConfigTester(self    ,						config_class=snake_case_    ,						has_text_modality=snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							self.config_tester.run_common_tests()

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_, UpperCamelCase_:				str						=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(snake_case_    ,						**snake_case_    ,						output_hidden_states=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_:				str						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_   )

			@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""   )
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							pass

			@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""   )
			def    lowerCAmelCase__						(							self			:							Dict   ):
							pass

			@unittest.skip(reason="""MaskFormer is not a generative model"""   )
			def    lowerCAmelCase__						(							self			:							Any   ):
							pass

			@unittest.skip(reason="""MaskFormer does not use token embeddings"""   )
			def    lowerCAmelCase__						(							self			:							str   ):
							pass

			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
			def    lowerCAmelCase__						(							self			:							Any   ):
							pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							pass

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_, UpperCamelCase_:				Tuple						=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:				Optional[Any]						=    model_class(snake_case_   )
											UpperCamelCase_:				Tuple						=    inspect.signature(model.forward   )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCamelCase_:				int						=    [*signature.parameters.keys()]

											UpperCamelCase_:				Optional[int]						=    ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]    ,						snake_case_   )

			@slow
			def    lowerCAmelCase__						(							self			:							Dict   ):
							for model_name in ["facebook/maskformer-swin-small-coco"]:
											UpperCamelCase_:				List[str]						=    MaskFormerModel.from_pretrained(snake_case_   )
											self.assertIsNotNone(snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Any						=    (self.model_tester.min_size,) * 2
							UpperCamelCase_:				Optional[int]						=    {
							    """pixel_values""": torch.randn((2, 3, *size)    ,						device=snake_case_   ),
							    """mask_labels""": torch.randn((2, 10, *size)    ,						device=snake_case_   ),
							    """class_labels""": torch.zeros(2    ,						10    ,						device=snake_case_   ).long(),
							}

							UpperCamelCase_:				Any						=    MaskFormerForInstanceSegmentation(MaskFormerConfig()   ).to(snake_case_   )
							UpperCamelCase_:				Any						=    model(**snake_case_   )
							self.assertTrue(outputs.loss is not None   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_, UpperCamelCase_:				List[Any]						=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(snake_case_    ,						**snake_case_    ,						output_hidden_states=snake_case_   )

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_, UpperCamelCase_:				str						=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:				Union[str, Any]						=    model_class(snake_case_   ).to(snake_case_   )
											UpperCamelCase_:				Dict						=    model(**snake_case_    ,						output_attentions=snake_case_   )
											self.assertTrue(outputs.attentions is not None   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							if not self.model_tester.is_training:
											return
							# only MaskFormerForInstanceSegmentation has the loss
							UpperCamelCase_:				Optional[Any]						=    self.all_model_classes[1]
							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs()

							UpperCamelCase_:				int						=    model_class(snake_case_   )
							model.to(snake_case_   )
							model.train()

							UpperCamelCase_:				Optional[Any]						=    model(snake_case_    ,						mask_labels=snake_case_    ,						class_labels=snake_case_   ).loss
							loss.backward()




			def    lowerCAmelCase__						(							self			:							Tuple   ):
							# only MaskFormerForInstanceSegmentation has the loss
							UpperCamelCase_:				Any						=    self.all_model_classes[1]
							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs()
							UpperCamelCase_:				Optional[int]						=    True
							UpperCamelCase_:				Dict						=    True

							UpperCamelCase_:				Dict						=    model_class(snake_case_   )
							model.to(snake_case_   )
							model.train()

							UpperCamelCase_:				Optional[int]						=    model(snake_case_    ,						mask_labels=snake_case_    ,						class_labels=snake_case_   )

							UpperCamelCase_:				List[Any]						=    outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							UpperCamelCase_:				Optional[int]						=    outputs.pixel_decoder_hidden_states[0]
							pixel_decoder_hidden_states.retain_grad()
							# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
							UpperCamelCase_:				Optional[int]						=    outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							UpperCamelCase_:				List[Any]						=    outputs.attentions[0]
							attentions.retain_grad()

							outputs.loss.backward(retain_graph=snake_case_   )

							self.assertIsNotNone(encoder_hidden_states.grad   )
							self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
							self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
							self.assertIsNotNone(attentions.grad   )




lowerCamelCase_    :			Optional[Any]    =	1E-4







def 							A__       (    )	->						List[str]:
				UpperCamelCase_:				str						=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
				return image





@require_vision
@slow
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			@cached_property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return (
							    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
							    if is_vision_available()
							    else None
							)

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				Union[str, Any]						=    MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""   ).to(snake_case_   )
							UpperCamelCase_:				Tuple						=    self.default_image_processor
							UpperCamelCase_:				List[str]						=    prepare_img()
							UpperCamelCase_:				Union[str, Any]						=    image_processor(snake_case_    ,						return_tensors="""pt"""   ).to(snake_case_   )
							UpperCamelCase_:				List[str]						=    inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
							# check size
							self.assertEqual(snake_case_    ,						(1, 3, 800, 1088)   )

							with torch.no_grad():
											UpperCamelCase_:				Optional[int]						=    model(**snake_case_   )

							UpperCamelCase_:				int						=    torch.tensor(
							    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]   ).to(snake_case_   )
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )

							UpperCamelCase_:				Optional[int]						=    torch.tensor(
							    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]   ).to(snake_case_   )
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )

							UpperCamelCase_:				Tuple						=    torch.tensor(
							    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]   ).to(snake_case_   )
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				str						=    (
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
							    .to(snake_case_   )
							    .eval()
							)
							UpperCamelCase_:				Dict						=    self.default_image_processor
							UpperCamelCase_:				Optional[int]						=    prepare_img()
							UpperCamelCase_:				int						=    image_processor(snake_case_    ,						return_tensors="""pt"""   ).to(snake_case_   )
							UpperCamelCase_:				Tuple						=    inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
							# check size
							self.assertEqual(snake_case_    ,						(1, 3, 800, 1088)   )

							with torch.no_grad():
											UpperCamelCase_:				Tuple						=    model(**snake_case_   )
							# masks_queries_logits
							UpperCamelCase_:				Any						=    outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape    ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    ,						)
							UpperCamelCase_:				Optional[Any]						=    [
							    [-1.373_7124, -1.772_4937, -1.936_4233],
							    [-1.597_7281, -1.986_7939, -2.152_3695],
							    [-1.579_5398, -1.926_9832, -2.09_3942],
							]
							UpperCamelCase_:				Tuple						=    torch.tensor(snake_case_   ).to(snake_case_   )
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )
							# class_queries_logits
							UpperCamelCase_:				str						=    outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape    ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
							UpperCamelCase_:				Dict						=    torch.tensor(
							    [
							        [1.6_512e00, -5.2_572e00, -3.3_519e00],
							        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
							        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
							    ]   ).to(snake_case_   )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				int						=    (
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""   )
							    .to(snake_case_   )
							    .eval()
							)
							UpperCamelCase_:				str						=    self.default_image_processor
							UpperCamelCase_:				int						=    prepare_img()
							UpperCamelCase_:				Union[str, Any]						=    image_processor(snake_case_    ,						return_tensors="""pt"""   ).to(snake_case_   )
							UpperCamelCase_:				List[Any]						=    inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0   )
							# check size
							self.assertEqual(snake_case_    ,						(1, 3, 800, 1088)   )

							with torch.no_grad():
											UpperCamelCase_:				Union[str, Any]						=    model(**snake_case_   )
							# masks_queries_logits
							UpperCamelCase_:				Optional[int]						=    outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape    ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    ,						)
							UpperCamelCase_:				List[Any]						=    [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
							UpperCamelCase_:				List[str]						=    torch.tensor(snake_case_   ).to(snake_case_   )
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )
							# class_queries_logits
							UpperCamelCase_:				Optional[Any]						=    outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape    ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)   )
							UpperCamelCase_:				Optional[int]						=    torch.tensor(
							    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]   ).to(snake_case_   )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						snake_case_    ,						atol=snake_case_   )   )




			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Optional[int]						=    (
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""   )
							    .to(snake_case_   )
							    .eval()
							)
							UpperCamelCase_:				Dict						=    self.default_image_processor

							UpperCamelCase_:				Optional[Any]						=    image_processor(
							    [np.zeros((3, 800, 1333)   ), np.zeros((3, 800, 1333)   )]    ,						segmentation_maps=[np.zeros((384, 384)   ).astype(np.floataa   ), np.zeros((384, 384)   ).astype(np.floataa   )]    ,						return_tensors="""pt"""    ,						)

							UpperCamelCase_:				Dict						=    inputs["""pixel_values"""].to(snake_case_   )
							UpperCamelCase_:				List[str]						=    [el.to(snake_case_   ) for el in inputs["""mask_labels"""]]
							UpperCamelCase_:				List[str]						=    [el.to(snake_case_   ) for el in inputs["""class_labels"""]]

							with torch.no_grad():
											UpperCamelCase_:				List[str]						=    model(**snake_case_   )

							self.assertTrue(outputs.loss is not None   )








import collections
import os
import re
from pathlib import Path


lowerCamelCase_    :			Optional[Any]    =	"""src/transformers"""


# Matches is_xxx_available()
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase_    :			int    =	re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCamelCase_    :			Any    =	re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase_    :			Any    =	re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase_    :			List[Any]    =	re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase_    :			Any    =	re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase_    :			Tuple    =	re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCamelCase_    :			Tuple    =	re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCamelCase_    :			Dict    =	re.compile(r"""^\s*try:""")
# Catches a line with else:
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""^\s*else:""")







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				if _re_test_backend.search(lowerCamelCase    ) is None:
								return None
				UpperCamelCase_:				Any						=    [b[0] for b in _re_backend.findall(lowerCamelCase    )]
				backends.sort()
				return "_and_".join(lowerCamelCase    )







def 							A__       (    lowerCamelCase    )	->						Union[str, Any]:
				with open(lowerCamelCase  ,     """r"""  ,     encoding="""utf-8"""  ,     newline="""\n"""    ) as f:
								UpperCamelCase_:				Dict						=    f.readlines()

				UpperCamelCase_:				Tuple						=    0
				while line_index < len(lowerCamelCase    ) and not lines[line_index].startswith("""_import_structure = {"""    ):
								line_index += 1

				# If this is a traditional init, just return.
				if line_index >= len(lowerCamelCase    ):
								return None

				# First grab the objects without a specific backend in _import_structure
				UpperCamelCase_:				Optional[int]						=    []
				while not lines[line_index].startswith("""if TYPE_CHECKING"""    ) and find_backend(lines[line_index]    ) is None:
								UpperCamelCase_:				Any						=    lines[line_index]
								# If we have everything on a single line, let's deal with it.
								if _re_one_line_import_struct.search(lowerCamelCase    ):
												UpperCamelCase_:				str						=    _re_one_line_import_struct.search(lowerCamelCase    ).groups()[0]
												UpperCamelCase_:				Tuple						=    re.findall(r"""\[([^\]]+)\]"""  ,     lowerCamelCase    )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(""", """    )]    )
												line_index += 1
												continue
								UpperCamelCase_:				Any						=    _re_import_struct_key_value.search(lowerCamelCase    )
								if single_line_import_search is not None:
												UpperCamelCase_:				Any						=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """    ) if len(lowerCamelCase    ) > 0]
												objects.extend(lowerCamelCase    )
								elif line.startswith(""" """ * 8 + """\""""    ):
												objects.append(line[9:-3]    )
								line_index += 1

				UpperCamelCase_:				Optional[Any]						=    {"""none""": objects}
				# Let's continue with backend-specific objects in _import_structure
				while not lines[line_index].startswith("""if TYPE_CHECKING"""    ):
								# If the line is an if not is_backend_available, we grab all objects associated.
								UpperCamelCase_:				Any						=    find_backend(lines[line_index]    )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]    ) is None:
												UpperCamelCase_:				Any						=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]    ) is None:
																line_index += 1

												line_index += 1

												UpperCamelCase_:				int						=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(""" """ * 4    ):
																UpperCamelCase_:				Tuple						=    lines[line_index]
																if _re_import_struct_add_one.search(lowerCamelCase    ) is not None:
																				objects.append(_re_import_struct_add_one.search(lowerCamelCase    ).groups()[0]    )
																elif _re_import_struct_add_many.search(lowerCamelCase    ) is not None:
																				UpperCamelCase_:				List[str]						=    _re_import_struct_add_many.search(lowerCamelCase    ).groups()[0].split(""", """    )
																				UpperCamelCase_:				str						=    [obj[1:-1] for obj in imports if len(lowerCamelCase    ) > 0]
																				objects.extend(lowerCamelCase    )
																elif _re_between_brackets.search(lowerCamelCase    ) is not None:
																				UpperCamelCase_:				Tuple						=    _re_between_brackets.search(lowerCamelCase    ).groups()[0].split(""", """    )
																				UpperCamelCase_:				List[Any]						=    [obj[1:-1] for obj in imports if len(lowerCamelCase    ) > 0]
																				objects.extend(lowerCamelCase    )
																elif _re_quote_object.search(lowerCamelCase    ) is not None:
																				objects.append(_re_quote_object.search(lowerCamelCase    ).groups()[0]    )
																elif line.startswith(""" """ * 8 + """\""""    ):
																				objects.append(line[9:-3]    )
																elif line.startswith(""" """ * 12 + """\""""    ):
																				objects.append(line[13:-3]    )
																line_index += 1

												UpperCamelCase_:				List[str]						=    objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				UpperCamelCase_:				List[str]						=    []
				while (
				    line_index < len(lowerCamelCase    )
				    and find_backend(lines[line_index]    ) is None
				    and not lines[line_index].startswith("""else"""    )
				):
								UpperCamelCase_:				List[str]						=    lines[line_index]
								UpperCamelCase_:				Union[str, Any]						=    _re_import.search(lowerCamelCase    )
								if single_line_import_search is not None:
												objects.extend(single_line_import_search.groups()[0].split(""", """    )    )
								elif line.startswith(""" """ * 8    ):
												objects.append(line[8:-2]    )
								line_index += 1

				UpperCamelCase_:				Any						=    {"""none""": objects}
				# Let's continue with backend-specific objects
				while line_index < len(lowerCamelCase    ):
								# If the line is an if is_backend_available, we grab all objects associated.
								UpperCamelCase_:				str						=    find_backend(lines[line_index]    )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]    ) is None:
												UpperCamelCase_:				List[Any]						=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]    ) is None:
																line_index += 1

												line_index += 1

												UpperCamelCase_:				int						=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(""" """ * 8    ):
																UpperCamelCase_:				Tuple						=    lines[line_index]
																UpperCamelCase_:				Union[str, Any]						=    _re_import.search(lowerCamelCase    )
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(""", """    )    )
																elif line.startswith(""" """ * 12    ):
																				objects.append(line[12:-2]    )
																line_index += 1

												UpperCamelCase_:				Optional[Any]						=    objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[Any]:

				def find_duplicates(lowerCamelCase    ):
								return [k for k, v in collections.Counter(lowerCamelCase    ).items() if v > 1]

				if list(import_dict_objects.keys()    ) != list(type_hint_objects.keys()    ):
								return ["Both sides of the init do not have the same backends!"]

				UpperCamelCase_:				Optional[int]						=    []
				for key in import_dict_objects.keys():
								UpperCamelCase_:				List[str]						=    find_duplicates(import_dict_objects[key]    )
								if duplicate_imports:
												errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''    )
								UpperCamelCase_:				Tuple						=    find_duplicates(type_hint_objects[key]    )
								if duplicate_type_hints:
												errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''    )

								if sorted(set(import_dict_objects[key]    )    ) != sorted(set(type_hint_objects[key]    )    ):
												UpperCamelCase_:				int						=    """base imports""" if key == """none""" else F'''{key} backend'''
												errors.append(F'''Differences for {name}:'''    )
												for a in type_hint_objects[key]:
																if a not in import_dict_objects[key]:
																				errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''    )
												for a in import_dict_objects[key]:
																if a not in type_hint_objects[key]:
																				errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''    )
				return errors







def 							A__       (    )	->						Tuple:
				UpperCamelCase_:				Optional[int]						=    []
				for root, _, files in os.walk(lowerCamelCase    ):
								if "__init__.py" in files:
												UpperCamelCase_:				Union[str, Any]						=    os.path.join(lowerCamelCase  ,     """__init__.py"""    )
												UpperCamelCase_:				Optional[int]						=    parse_init(lowerCamelCase    )
												if objects is not None:
																UpperCamelCase_:				Any						=    analyze_results(*lowerCamelCase    )
																if len(lowerCamelCase    ) > 0:
																				UpperCamelCase_:				Any						=    F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
																				failures.append("""\n""".join(lowerCamelCase    )    )
				if len(lowerCamelCase    ) > 0:
								raise ValueError("""\n\n""".join(lowerCamelCase    )    )







def 							A__       (    )	->						Any:
				UpperCamelCase_:				List[Any]						=    []
				for path, directories, files in os.walk(lowerCamelCase    ):
								for folder in directories:
												# Ignore private modules
												if folder.startswith("""_"""    ):
																directories.remove(lowerCamelCase    )
																continue
												# Ignore leftovers from branches (empty folders apart from pycache)
												if len(list((Path(lowerCamelCase    ) / folder).glob("""*.py"""    )    )    ) == 0:
																continue
												UpperCamelCase_:				str						=    str((Path(lowerCamelCase    ) / folder).relative_to(lowerCamelCase    )    )
												UpperCamelCase_:				Optional[Any]						=    short_path.replace(os.path.sep  ,     """."""    )
												submodules.append(lowerCamelCase    )
								for fname in files:
												if fname == "__init__.py":
																continue
												UpperCamelCase_:				Dict						=    str((Path(lowerCamelCase    ) / fname).relative_to(lowerCamelCase    )    )
												UpperCamelCase_:				Optional[int]						=    short_path.replace(""".py"""  ,     """"""    ).replace(os.path.sep  ,     """."""    )
												if len(submodule.split("""."""    )    ) == 1:
																submodules.append(lowerCamelCase    )
				return submodules


lowerCamelCase_    :			Optional[int]    =	[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
    """models.esm.openfold_utils""",
]







def 							A__       (    )	->						List[str]:
				# This is to make sure the transformers module imported is the one in the repo.
				from transformers.utils import direct_transformers_import

				UpperCamelCase_:				Optional[Any]						=    direct_transformers_import(lowerCamelCase    )

				UpperCamelCase_:				Tuple						=    set(transformers._import_structure.keys()    )
				# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
				# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
				# (potentiall re-) add them.
				with open(os.path.join(lowerCamelCase  ,     """__init__.py"""    )  ,     """r"""    ) as f:
								UpperCamelCase_:				List[Any]						=    f.read()
				import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""  ,     lowerCamelCase    )    )    )

				UpperCamelCase_:				Dict						=    [
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(lowerCamelCase    ) > 0:
								UpperCamelCase_:				Dict						=    """\n""".join(F'''- {module}''' for module in module_not_registered    )
								raise ValueError(
								    """The following submodules are not properly registed in the main init of Transformers:\n"""
								    F'''{list_of_modules}\n'''
								    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""    )


if __name__ == "__main__":
							check_all_inits()
							check_submodules()






